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author | Gian Marco Iodice <gianmarco.iodice@arm.com> | 2019-03-11 16:07:12 +0000 |
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committer | Gian Marco Iodice <gianmarco.iodice@arm.com> | 2019-04-01 11:28:12 +0000 |
commit | 62251f71792c06dbe4c9d1985816ba15bcad14e4 (patch) | |
tree | d9d3778a2431486bffbcc80bd2cc51f3000ef116 /tests/validation/fixtures | |
parent | b4a44ff3aa98d2b51f1621a7525db3f81108a1bd (diff) | |
download | ComputeLibrary-62251f71792c06dbe4c9d1985816ba15bcad14e4.tar.gz |
COMPMID-2002: Implement CLGEMMLowpMatrixMultiplyReshapedOnlyRHS - Transposed
Change-Id: I3907d151107766dc34749fe5710d7219e810b39f
Signed-off-by: Gian Marco Iodice <gianmarco.iodice@arm.com>
Reviewed-on: https://review.mlplatform.org/c/875
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Giuseppe Rossini <giuseppe.rossini@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
Diffstat (limited to 'tests/validation/fixtures')
-rw-r--r-- | tests/validation/fixtures/GEMMLowpFixture.h | 208 |
1 files changed, 208 insertions, 0 deletions
diff --git a/tests/validation/fixtures/GEMMLowpFixture.h b/tests/validation/fixtures/GEMMLowpFixture.h index 90a4b5cf40..5793ebdd2d 100644 --- a/tests/validation/fixtures/GEMMLowpFixture.h +++ b/tests/validation/fixtures/GEMMLowpFixture.h @@ -611,6 +611,214 @@ protected: TensorType _target{}; SimpleTensor<int32_t> _reference{}; }; + +template <typename TensorType, typename AccessorType, typename ReshapeRHSFunctionType, typename GEMMFunctionType> +class GEMMLowpMatrixMultiplyReshapedOnlyRHSValidationFixture : public framework::Fixture +{ +public: + template <typename...> + void setup(unsigned int m, unsigned int n, unsigned int k, unsigned int batch_size, unsigned int m0, unsigned int n0, unsigned int k0, unsigned int h0, bool interleave_rhs, bool transpose_rhs) + { + GEMMLHSMatrixInfo lhs_info; + lhs_info.m0 = m0; + lhs_info.k0 = k0; + + GEMMRHSMatrixInfo rhs_info; + rhs_info.n0 = n0; + rhs_info.k0 = k0; + rhs_info.h0 = h0; + rhs_info.interleave = interleave_rhs; + rhs_info.transpose = transpose_rhs; + + // Set the tensor shapes for LHS and RHS matrices + const TensorShape lhs_shape(k, m, batch_size); + const TensorShape rhs_shape(n, k, batch_size); + + _target = compute_target(lhs_shape, rhs_shape, lhs_info, rhs_info); + _reference = compute_reference(lhs_shape, rhs_shape); + } + +protected: + template <typename U> + void fill(U &&tensor, int i) + { + // Between 1 and 254 in order to avoid having -128 and 128 for the DOT product path + std::uniform_int_distribution<> distribution(1, 254); + library->fill(tensor, distribution, i); + } + + TensorType compute_target(const TensorShape &lhs_shape, const TensorShape &rhs_shape, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info) + { + // Create tensors + TensorType lhs = create_tensor<TensorType>(lhs_shape, DataType::QASYMM8, 1); + TensorType rhs = create_tensor<TensorType>(rhs_shape, DataType::QASYMM8, 1); + TensorType rhs_reshaped; + TensorType dst; + + const unsigned int M = lhs_shape[1]; + const unsigned int N = rhs_shape[0]; + const unsigned int K = lhs_shape[0]; + + // The output tensor will be auto-initialized within the function + + // Create and configure function + ReshapeRHSFunctionType reshape_rhs; + GEMMFunctionType gemm; + reshape_rhs.configure(&rhs, &rhs_reshaped, rhs_info); + gemm.configure(&lhs, &rhs_reshaped, &dst, lhs_info, rhs_info, GEMMReshapeInfo(M, N, K)); + + ARM_COMPUTE_EXPECT(lhs.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(rhs.info()->is_resizable(), framework::LogLevel::ERRORS); + + // Allocate tensors + lhs.allocator()->allocate(); + rhs.allocator()->allocate(); + rhs_reshaped.allocator()->allocate(); + dst.allocator()->allocate(); + + ARM_COMPUTE_EXPECT(!lhs.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(!rhs.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(!rhs_reshaped.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS); + + // Fill tensors + fill(AccessorType(lhs), 0); + fill(AccessorType(rhs), 1); + + // Compute GEMM + reshape_rhs.run(); + gemm.run(); + + return dst; + } + + SimpleTensor<int32_t> compute_reference(const TensorShape &lhs_shape, const TensorShape &rhs_shape) + { + TensorShape dst_shape = lhs_shape; + dst_shape[0] = rhs_shape[0]; + dst_shape[1] = lhs_shape[1]; + + // Create reference + SimpleTensor<uint8_t> lhs{ lhs_shape, DataType::QASYMM8, 1 }; + SimpleTensor<uint8_t> rhs{ rhs_shape, DataType::QASYMM8, 1 }; + + // Fill reference + fill(lhs, 0); + fill(rhs, 1); + + return reference::gemmlowp_matrix_multiply_core<int32_t, uint8_t>(lhs, rhs, dst_shape, 0, 0); + } + + TensorType _target{}; + SimpleTensor<int32_t> _reference{}; +}; + +template <typename TensorType, typename AccessorType, typename ReshapeRHSFunctionType, typename GEMMFunctionType> +class GEMMLowpMatrixMultiplyReshapedOnlyRHS3DValidationFixture : public framework::Fixture +{ +public: + template <typename...> + void setup(unsigned int m_w, unsigned int m_h, unsigned int n, unsigned int k, unsigned int batch_size, unsigned int m0, unsigned int n0, unsigned int k0, unsigned int h0, + bool interleave_rhs, bool transpose_rhs) + { + GEMMLHSMatrixInfo lhs_info; + lhs_info.m0 = m0; + lhs_info.k0 = k0; + + GEMMRHSMatrixInfo rhs_info; + rhs_info.n0 = n0; + rhs_info.k0 = k0; + rhs_info.h0 = h0; + rhs_info.interleave = interleave_rhs; + rhs_info.transpose = transpose_rhs; + + // In case of GEMM3D, m is the product between m_w and m_h + const unsigned int m = m_w * m_h; + + // Set the tensor shapes for LHS and RHS matrices + const TensorShape lhs_shape(k, m, batch_size); + const TensorShape rhs_shape(n, k, batch_size); + + _target = compute_target(lhs_shape, rhs_shape, lhs_info, rhs_info, m_h); + _reference = compute_reference(lhs_shape, rhs_shape, m_h); + } + +protected: + template <typename U> + void fill(U &&tensor, int i) + { + // Between 1 and 254 in order to avoid having -128 and 128 for the DOT product path + std::uniform_int_distribution<> distribution(1, 254); + library->fill(tensor, distribution, i); + } + + TensorType compute_target(const TensorShape &lhs_shape, const TensorShape &rhs_shape, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info, unsigned int m_h) + { + // Create tensors + TensorType lhs = create_tensor<TensorType>(lhs_shape, DataType::QASYMM8, 1); + TensorType rhs = create_tensor<TensorType>(rhs_shape, DataType::QASYMM8, 1); + TensorType rhs_reshaped; + TensorType dst; + + const unsigned int M = lhs_shape[1]; + const unsigned int N = rhs_shape[0]; + const unsigned int K = lhs_shape[0]; + + // The output tensor will be auto-initialized within the function + + // Create and configure function + ReshapeRHSFunctionType reshape_rhs; + GEMMFunctionType gemm; + reshape_rhs.configure(&rhs, &rhs_reshaped, rhs_info); + gemm.configure(&lhs, &rhs_reshaped, &dst, lhs_info, rhs_info, GEMMReshapeInfo(M, N, K, 1, 1, m_h)); + + ARM_COMPUTE_EXPECT(lhs.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(rhs.info()->is_resizable(), framework::LogLevel::ERRORS); + + // Allocate tensors + lhs.allocator()->allocate(); + rhs.allocator()->allocate(); + rhs_reshaped.allocator()->allocate(); + dst.allocator()->allocate(); + + ARM_COMPUTE_EXPECT(!lhs.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(!rhs.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(!rhs_reshaped.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS); + + // Fill tensors + fill(AccessorType(lhs), 0); + fill(AccessorType(rhs), 1); + + // Compute GEMM + reshape_rhs.run(); + gemm.run(); + + return dst; + } + + SimpleTensor<int32_t> compute_reference(const TensorShape &lhs_shape, const TensorShape &rhs_shape, unsigned int m_h) + { + TensorShape dst_shape = lhs_shape; + dst_shape.set(0, rhs_shape[0]); + dst_shape.set(1, lhs_shape[1] / m_h); + dst_shape.set(2, m_h); + dst_shape.set(3, lhs_shape[2]); + + // Create reference + SimpleTensor<uint8_t> lhs{ lhs_shape, DataType::QASYMM8, 1 }; + SimpleTensor<uint8_t> rhs{ rhs_shape, DataType::QASYMM8, 1 }; + + // Fill reference + fill(lhs, 0); + fill(rhs, 1); + + return reference::gemmlowp_matrix_multiply_core<int32_t, uint8_t>(lhs, rhs, dst_shape, 0, 0); + } + + TensorType _target{}; + SimpleTensor<int32_t> _reference{}; +}; } // namespace validation } // namespace test } // namespace arm_compute |